CN106646096B - Transformer fault classification and recognition methods based on vibration analysis method - Google Patents
Transformer fault classification and recognition methods based on vibration analysis method Download PDFInfo
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Abstract
The invention discloses a kind of transformer fault classification and recognition methods based on vibration analysis method, comprising the following steps: S1: choosing transformer test object, and acquiring transformer vibration signal under different conditions is sample data；S2: intrinsic mode functions are obtained using ensemble empirical mode decomposition method calculating sample data is gathered in Hilbert-Huang transform；S3: characteristic vector V in intrinsic mode functions component is extracted；S4: dimensionality reduction is carried out to characteristic vector using Principal Component Analysis, coordinate projection is into two dimensional image；S5: classified adjacent to method to sample data using K；S6: test sample is calculated at a distance from original sample using range formula；S7: pattern-recognition is carried out；S8: corresponding transformer fault type in output mode identification；It can intuitively and effectively judge running state of transformer.
Description
Technical field
The present invention relates to electric device maintenance fields, and in particular to a kind of transformer fault classification based on vibration analysis method
And recognition methods.
Background technique
In the various equipment of electric system, transformer is expensive and important equipment, and reliability is for guaranteeing power grid
It is safely operated significant.Transformer fault statistics over the years show transformer winding and iron core is the more portion that breaks down
The Accident of Transformer that part, winding and iron core cause proportion in the total accident of transformer reaches 70%.
Currently, the method for detection running state of transformer is increasing.Short circuit impedance method passes through the electricity of off-line measurement winding
Resist and observe its change in impedance value to judge winding deformation situation, but this method sensitivity is low, poor reliability.Low Voltage Impulse Method,
It is mainly used in field test test, however this method has vulnerable to interference effect, need to correct, tests the special cloth of circuit requirement
The defects of setting the repeatability to guarantee test.Frequency Response Analysis method is carried out by the frequency response curve of 3 phase winding of measuring transformer
Test transformer winding deformation degree overcomes some defects that Low Voltage Impulse Method has repeatability difference, but this method can only
Offline inspection is carried out to transformer.But three of the above method can only all realize offline inspection, it therefore, can be with there is an urgent need to one kind
The method for carrying out carrying out live detection to transformer fault.
Existing set ensemble empirical mode decomposition method, i.e. EEMD method extract feature vector and the utilization of transformer state
Fisher diagnostic method, which carries out failure modes, may be implemented the live detection to transformer fault.But transformation is extracted according to EEMD
The characteristic vector of device vibration signal, characteristic vector are multi-group datas, and when test sample is more, and test sample can become one
Multidimensional numerical.Multidimensional numerical is unfavorable for calculating, while the observation being also unfavorable in reality；Fisher diagnostic method can only be realized simultaneously
Linear classification cannot achieve Nonlinear Classification, due to the uncertainty of test measurement data in test data, inevitably will appear
Wrong data, these wrong data influence whether the effect of linear classification.
Summary of the invention
In order to solve the above-mentioned technical problem the present invention provides a kind of transformer fault classification and knowledge based on vibration analysis method
Other method.
The present invention is achieved through the following technical solutions:
Transformer fault classification and recognition methods based on vibration analysis method, comprising the following steps:
S1: choosing transformer test object, and acquiring transformer vibration signal under different conditions is sample data；
S2: eigen mode letter is obtained using ensemble empirical mode decomposition method calculating sample data is gathered in Hilbert-Huang transform
Number；
S3: characteristic vector V in intrinsic mode functions component is extracted；
S4: dimensionality reduction is carried out to characteristic vector using Principal Component Analysis, coordinate projection is into two dimensional image；
S5: classified adjacent to method to sample data using K；
S6: test sample is calculated at a distance from original sample using range formula；
S7: pattern-recognition is carried out；
S8: corresponding transformer fault type in output mode identification.
Due to the presence of transformer winding and core structure nonlinear characteristic, transformer vibration signal can show brighter
Aobvious is non-linear.Traditional Time-Frequency Analysis Method is in analysis nonlinear and non local boundary value problem Shortcomings.The present invention uses principal component
Analytic approach and KNN Classification and Identification method realize the intuitive taxonomy to test sample and pattern-recognition.It, can for test sample data
To realize intuitive projection on 2d, reach intuitive taxonomy effect, the minute quantity wrong data in classification will not shadow
Ring the effect to KNN classification and pattern-recognition.
In step s 2, sample data is calculated using set ensemble empirical mode decomposition method in Hilbert-Huang transform to obtain
The step of intrinsic mode functions are as follows:
To the Gaussian white noise sequence n that M times is added in original signal x (t)i(t), (i=1,2 ..., M), it may be assumed that
Xi(t)=x (t)+ni(t)；
To Xi(t) empirical mode decomposition is carried out respectively, obtains the component and surplus of each intrinsic mode functions, it may be assumed that
Wherein hijAfter white Gaussian noise is added for i-th, to Xi(t) j-th of the intrinsic mode functions decomposed point
Amount；rinAfter white Gaussian noise is added for i-th, to Xi(t) remainder after being decomposed；N is Decomposition order；
Using the zero-mean principle of white Gaussian noise frequency spectrum, the corresponding intrinsic mode functions component of above step is carried out overall
Average calculating operation, eliminate white Gaussian noise influences as time domain distribution reference structure bring, intrinsic after finally obtained EEMD
The component of modular function are as follows:
In formula, hj(t) it indicates to carry out j-th of intrinsic mode functions component that EEMD is decomposed to original signal；M is to be added
The number of white noise.Wherein, intrinsic mode functions, that is, IMF；Gathering ensemble empirical mode decomposition method in Hilbert-Huang transform is
EEMD；Empirical mode decomposition, that is, EMD.
In step s3, characteristic vector V calculation method in intrinsic mode functions component is extracted, each IMF component is chosen and carries out
Hilbert analysis, and to the amplitude constitutive characteristic vector V under corresponding:
V=[v1, v2..., vz]；
In formula, Z indicates characteristic vector number, AjIt (i) is the amplitude of j-th of intrinsic mode functions component.
In step s 4, steps are as follows to characteristic vector progress dimensionality reduction for Principal Component Analysis:
Using EEMD extract transformer vibration signal under 3 kinds of normal condition, winding failure, iron core failure operating conditions feature to
Amount
V=[v1, v2..., vz]；
It is directed to 3 kinds of operating conditions simultaneously, it is each to select m group empirically sample, obtain the training matrix R of 4m × z；
Centralization is done to training matrix R to handle to obtain matrix A=[aij],
Wherein, aijIt is the data in R after element center, vijIt is the sample value that the i-th row jth arranges in training matrix R,It is
The mean value of every row in training matrix R；
Using calculate centralization matrix A covariance matrix S,
The characteristic value and feature vector of covariance matrix S in calculating carries out maximum value arrangement, takes the first two nonzero eigenvalue
Corresponding feature vector [a1, a2] it is projecting direction；
To [a shown in R progress1, a2] direction projection transform, obtain the X-Y scheme of training sample:
In formula, T representing matrix transposition, Y1、Y2The respectively cross, ordinate of two-dimensional projection.
Principal component analytical method, that is, PCA is the position distribution according to sample point in multi-dimensional model space, most generous with variance
To the feature extraction and data compression of data are realized as vector is differentiated in the i.e. maximum direction that changes in space of sample point.
In step s 6, it calculates test sample and the range formula of original sample is as follows:
Test sample vector Yt=[yt1, yt2]。
In the step s 7, mode identification method is as follows:
The K neighbour apart from K relatively small sample as test sample is selected, if w is transformer state type, K is a
In group sample, wherein come from w1The sample of Status Type has M1It is a, come from w2The sample of Status Type has M2It is a ..., come from wc
The sample of Status Type has McIt is a, if k1, k2..., kcIt is to belong to w in k neighbour respectively1, w2..., wcThe sample number of class, then it is fixed
Justice differentiates letter are as follows:
gi(Vt)=ki, i=1,2 ..., c；
If gj(Vt)=max (kt), then test sample Vi∈ωj, the corresponding variable-pressure operation state of test sample x is wj transformation
Device Status Type.
K-nearest neighbor, that is, K-Nearest Neighbor, KNN is to calculate sample x to be sorted and instruction according to distance metric function
Practice the distance for concentrating each training sample, to calculated distance-taxis, selection and K nearest training sample of sample to be sorted
The K arest neighbors as x.
The EEMD method that Huang is proposed has the characteristic of zero-mean using white noise, and EEMD method is in signal to be decomposed
Empirical mode decomposition is carried out after white noise is added, after multiple averaging, noise will be supported mutually decomposition result, integrate the knot of mean value
Fruit is as final IMF component.A large amount of transformer winding vibration signal datas are tested, the intrinsic mode functions that EEMD is obtained
Intrinsic Mode IMF can embody the vibration characteristics of transformer winding well, and have specific physical significance.Therefore,
Transformer vibration signal, which is carried out EEMD selection, can effectively reflect the IMF of transformer vibration information, calculate its instantaneous frequency and wink
Between amplitude as characteristic vector, can be with quantificational expression transformer current state.
Compared with prior art, the present invention at least having the following advantages and benefits:
The present invention is analyzed using vibration signal row of the EEMD to transformer, extracts Transformer nominal situation, winding
Vibration signal extracts characteristic vector under deformation, iron core failure different conditions, and vibration signal is extracted using principal component analysis
Characteristic vector carries out Dimension Reduction Analysis, and characteristic vector is projected in intuitive X-Y scheme, finally uses KNN classifying identification method
Classification and pattern-recognition are carried out to data, to intuitively and effectively judge running state of transformer.
Detailed description of the invention
Attached drawing described herein is used to provide to further understand the embodiment of the present invention, constitutes one of the application
Point, do not constitute the restriction to the embodiment of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart of the method for the present invention.
Fig. 2 is the sample data result figure in the embodiment of the present invention 2.
Fig. 3 is the test sample pattern recognition result figure in the embodiment of the present invention 2.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment and attached drawing, to this
Invention is described in further detail, and exemplary embodiment of the invention and its explanation for explaining only the invention, are not made
For limitation of the invention.
Embodiment 1
Transformer fault classification and recognition methods based on vibration analysis method as shown in Figure 1, comprising the following steps:
S1: choosing corresponding transformer is subjects, and acquiring transformer vibration signal under different conditions is sample data；
S2: eigen mode letter is obtained using ensemble empirical mode decomposition method calculating sample data is gathered in Hilbert-Huang transform
Number；
S3: characteristic vector V in intrinsic mode functions component is extracted；
S4: dimensionality reduction is carried out to characteristic vector using Principal Component Analysis, coordinate projection is into two dimensional image；
S5: classified adjacent to method to sample data using K；
S6: test sample is calculated at a distance from original sample using range formula；
S7: pattern-recognition is carried out；
S8: corresponding transformer fault type in output mode identification.
Embodiment 2,
On the basis of the above embodiments, specific:
In step s 2, sample data is calculated using set ensemble empirical mode decomposition method in Hilbert-Huang transform to obtain
The step of intrinsic mode functions are as follows:
To the Gaussian white noise sequence n that M times is added in original signal x (t)i(t), (i=1,2 ..., M), it may be assumed that
Xi(t)=x (t)+ni(t)；
To Xi(t) EMD is carried out respectively, obtains the component and surplus of each intrinsic mode functions, it may be assumed that
Wherein hijAfter white Gaussian noise is added for i-th, to Xi(t) j-th of the intrinsic mode functions decomposed point
Amount；rinAfter white Gaussian noise is added for i-th, to Xi(t) remainder after being decomposed；N is Decomposition order；
Using the zero-mean principle of white Gaussian noise frequency spectrum, the corresponding intrinsic mode functions component of above step is carried out overall
Average calculating operation, eliminate white Gaussian noise influences as time domain distribution reference structure bring, intrinsic after finally obtained EEMD
The component of modular function are as follows:
In formula, hj(t) it indicates to carry out j-th of intrinsic mode functions component that EEMD is decomposed to original signal；N is to be added
The number of white noise.
In step s3, characteristic vector V calculation formula in intrinsic mode functions component: V=[v is extracted1, v2..., vz]；
In formula, AjIt (i) is the amplitude of j-th of intrinsic mode functions component.
In step s 4, steps are as follows to characteristic vector progress dimensionality reduction for Principal Component Analysis:
Using EEMD extract transformer vibration signal under 3 kinds of normal condition, winding failure, iron core failure operating conditions feature to
Amount
V=[v1, v2..., vz]；
It is directed to 3 kinds of operating conditions simultaneously, it is each to select m group empirically sample, obtain the training matrix R of 4m × z；
Centralization is done to training matrix R to handle to obtain matrix A=[aij],
Wherein, aijIt is the data in R after element center, vijIt is the sample value that the i-th row jth arranges in training matrix R,It is
The mean value of every row in training matrix R；
Using calculate centralization matrix A covariance matrix S,
The characteristic value and feature vector of covariance matrix S in calculating carries out maximum value arrangement, takes the first two nonzero eigenvalue
Corresponding feature vector [a1, a2] it is projecting direction；
To [a shown in R progress1, a2] direction projection transform, obtain the X-Y scheme of training sample；
In formula, Y1、Y2The respectively cross, ordinate of two-dimensional projection.
In step s 6, it calculates test sample and the range formula of original sample is as follows:
Test sample vector Yt=[yt1, yt2]。
In the step s 7, mode identification method is as follows:
The K neighbour apart from K relatively small sample as test sample is selected, if w is transformer state type, K is a
In group sample, w is come from1The sample of Status Type has M1It is a, come from w2The sample of Status Type has M2It is a ..., come from wcState class
The sample of type has McIt is a to come from, if k1, k2..., kcIt is to belong to w in k neighbour respectively1, w2..., wcThe sample number of class, then define
Differentiate letter are as follows:
gi(Vt)=ki, i=1,2 ..., c；
If gj(Vt)=max (kt), then test sample Vi∈ωj, the corresponding variable-pressure operation state of test sample x is wjTransformation
Device Status Type.
To verify the above-mentioned transformer fault detection method based on principal component analysis and KNN classifying identification method, this test
It is analyzed and researched using the vibration data that a 110kv three-phase transformer of Sichuan Guangan Utilities Electric Co. obtains.In experiment,
By 3 ICP type acceleration vibrating sensors, sensitivity 100mv/g is individually positioned in transformer height in a manner of permanent magnet
Press on side box wall, in the middle part of the corresponding tank wall of every phase winding, bottom and wall sides position carry out sampled data.Vibration data is adopted
Sample frequency is 25.6kHz.Simulation result is as shown in Figures 2 and 3.It is proved by test, this method may be implemented to transformer just
Normal state, winding deformation, iron core failure three state intuitive taxonomy, and quick automatic mode identification is carried out to test sample.This
Invention emulation case is only intended to help to illustrate the present invention.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention
Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include
Within protection scope of the present invention.
Claims (4)
1. transformer fault classification and recognition methods based on vibration analysis method, which comprises the following steps:
S1: choosing transformer test object, and acquiring transformer vibration signal under different conditions is sample data；
S2: intrinsic mode functions are obtained using ensemble empirical mode decomposition method calculating sample data is gathered in Hilbert-Huang transform；
S3: characteristic vector V in intrinsic mode functions component is extracted；
S4: dimensionality reduction is carried out to characteristic vector using Principal Component Analysis, coordinate projection is into two dimensional image；
S5: classified adjacent to method to sample data using K；
S6: test sample is calculated at a distance from original sample using range formula；
S7: pattern-recognition is carried out；
S8: corresponding transformer fault type in output mode identification；
In the step S4, Principal Component Analysis carries out dimensionality reduction to characteristic vector, and steps are as follows:
Transformer vibration signal feature vector V under 3 kinds of normal condition, winding failure, iron core failure operating conditions is extracted using EEMD
=[v1, v2..., vz]；
It is directed to 3 kinds of operating conditions simultaneously, it is each to select m group empirically sample, obtain the training matrix R of 4m × z；
Centralization is done to training matrix R to handle to obtain matrix A=[aij],
Wherein, aijIt is the data in R after element center, vijIt is the sample value that the i-th row jth arranges in training matrix R, viIt is trained
The mean value of every row in matrix R；
Using calculate centralization matrix A covariance matrix S,
The characteristic value and feature vector of covariance matrix S in calculating carries out maximum value arrangement, takes the first two nonzero eigenvalue corresponding
Feature vector [α1, α2] it is projecting direction；
To [α shown in R progress1, α2] direction projection transform, obtain the X-Y scheme of training sample；
In formula, T representing matrix transposition, Y1、Y2The respectively cross, ordinate of two-dimensional projection；
In the step S7, mode identification method is as follows:
K neighbour of the K sample as test sample is selected, if w is transformer state type, in K group sample, from w1Shape
The sample of state type has M1It is a, come from w2The sample of Status Type has M2It is a ..., come from wcThe sample of Status Type has McIt is a, if
k1, k2..., kcIt is to belong to w in k neighbour respectively1, w2..., wcThe sample number of class then defines differentiation letter are as follows:
gi(Vt)=ki, i=1,2 ..., c；
If gj(Vt)=max (ki), then test sample Vi∈ωj, the corresponding variable-pressure operation state of test sample x is wjTransformer shape
State type.
2. the transformer fault classification and recognition methods according to claim 1 based on vibration analysis method, it is characterised in that:
In step s 2, sample data is calculated using set ensemble empirical mode decomposition method in Hilbert-Huang transform obtain eigen mode letter
Several steps are as follows:
To the Gaussian white noise sequence n that M times is added in original signal x (t)i(t), (i=1,2 ..., M) obtains Xi(t), it may be assumed that
Xi(t)=x (t)+ni(t)；
To Xi(t) empirical mode decomposition is carried out respectively, obtains the component and surplus of each intrinsic mode functions, it may be assumed that
Wherein hijAfter white Gaussian noise is added for i-th, to Xi(t) j-th of the intrinsic mode functions component decomposed；rin
After white Gaussian noise is added for i-th, to Xi(t) remainder after being decomposed；N is Decomposition order；
Using the zero-mean principle of white Gaussian noise frequency spectrum, the corresponding intrinsic mode functions component of above step is subjected to population mean
Operation, the component of the intrinsic mode functions after obtained EEMD are as follows:
In formula, hj(t) it indicates to carry out j-th of intrinsic mode functions component that EEMD is decomposed to original signal；M is that white noise is added
The number of sound.
3. the transformer fault classification and recognition methods according to claim 1 based on vibration analysis method, it is characterised in that:
In step s3, characteristic vector V calculation formula in intrinsic mode functions component: V=[v is extracted1, v2..., vz]；
In formula, Z indicates characteristic vector number；AjIt (i) is the amplitude of j-th of intrinsic mode functions component.
4. the transformer fault classification and recognition methods according to claim 1 based on vibration analysis method, it is characterised in that:
In step s 6, it calculates test sample and the range formula of original sample is as follows:
Test sample vector Yt=[yt1, yt2]。
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